An endpoint adjudication committee for the assessment of computed tomography scans in fracture healing
Bibliographic record
Abstract
INTRODUCTION: Endpoint Adjudication Committees (EACs) benefit the quality of randomized control trials (RCTs) where outcomes depend on subjective interpretations. However, assembling a committee to adjudicate large datasets is cumbersome. In a recent RCT, the primary outcome was time to union following operative fixation of scaphoid non-union, with real or placebo adjunctive ultrasound treatment. Union status was determined with computed tomography (CT) scans interpreted by treating surgeons and radiologists. An EAC was established to deliberate discrepancies between radiologists' and surgeons' interpretations of union status. METHODS: Three hundred sixty-four CT scans from 142 participants were collected in the RCT. The treating surgeon and an MSK radiologist categorized images by percent-union (0 %, 1-24 %, 25-49 %, 50-74 %, 75-99 %, 100 %). Union was defined as at least 50 % trabecular bridging. The EAC adjudicated those images that were deemed major discrepancies. The committee was composed of three members assembled by the committee chair, an MSK radiologist. A charter was established to guide the adjudication process. Ten minutes were allotted to each scan, including 2-3 min of an independent adjudicator's review, followed by 5-7 min of committee discussion to reach a diagnosis. RESULTS: Adjudicators spent an average of seven minutes on each scan. The EAC assessed 101 CT scans from 69 patients collected across five study sites: four scans from the agreed upon group as practice interpretations, 75 major discrepancies, and 22 missing interpretations from either the initial MSK radiologist, the treating orthopaedic surgeon, or both. These were adjudicated for final union status. Twenty-eight of the images with major discrepancies were adjudicated to union, and 47 to non-union. Adjudication changed the primary outcome of time to union in 40/142 (28 %) of study participants. CONCLUSION: This adjudication process provides a valuable research tool for reference by other clinical investigators whose RCTs' outcomes are dependent on interpretation of radiographic images.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.335 | 0.314 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".